

import numpy as np

censor_fill_value = 0.
ens_dim = 1
dist_type = ('gamma', 'gamma') # first for forecasts, second for observations
cens_thres = (0.2, 0.2) # default censoring threshold
# set the number of ensemble for the Bernoulli-Gamma-Gaussian model
n_number = 500 # number of ensemble for the BGG model
fix_rnd_seed = 42

param_nms = {
    'gamma': ['a', 'loc', 'scale'],
    'norm': ['loc', 'scale'],
    'pearson3': ['skew', 'loc', 'scale'],
    'kappa4': ['h', 'k', 'loc', 'scale'],
    'lognorm' : ['s', 'loc', 'scale'],
    'genextreme': ['c', 'loc', 'scale'],
    'genlogistic': ['c', 'loc', 'scale'],
}
least_cens_coeff = 0.5
sample_size_tol = 10
invalid_para = np.nan